My method for generating startup ideas is notice the things that I hate. For example, I hate shaving, and would kill for a device that I can stick my face into and become clean-shaven one minute later. I also hate choosing the shower temperature -- I want the shower learn the temperatures I like. I also hate not knowing the definition of terms in government websites. I can't stand passwords. Etc. This feeling is my indicator of a good potential startup idea.
Many comments expressed concern about the alleged inappropriateness
of the title. Even the no-free
lunch theorem has been invoked, and words like SVM mentioned.
However: The original title, "Neural Networks officially best at object
recognition", is much more appropriate than the current title, because
it is by far the hardest vision contest. It is nearly two
orders of magnituder larger and harder than other contests,
which is why the winner of this contest is best at object recognition.
The original title is much more accurate and should be restored.
Second, the gap between the first and the second entry is so obviously
huge (25% error vs 15% error), that it cannot be bridged with simple "feature
engineering". Neural networks win precisely because they look at the
data, and choose the best possible features. The best human feature
engineers could not come close to a relentless data-hungry algorithm.
Third, there was mention of the no-free lunch theorem and of how one
cannot tell which methods are better. That
theorem says that learning is impossible on data that has
no structure, which is true but irrelevant. What's
relevant that on the "specific" problem of object recognition
as represented by this 1-million large dataset, neural networks
are the best method.
Finally, if somebody makes SVMs deep, they will become more like neural
networks and do better. Which is the point.
This is the beginning of the neural networks revolution in computer vision.
Actually, it does, since the difference in performance between entry #1 and entry #2 is so huge (25% error vs 15% error!), and since this is by far the hardest computer vision challenge yet!
These findings should not be misinterpreted, since it is indeed impossible to reach any goals. A person in their late 20s is extremely unlikely to become an olympic swimmer or a squash player because of physical limitations. And some people will relentlessly try and never succeed.
But believing that you can change is useful because it makes it easier to persist in my efforts to change. Because if I believed that change is impossible, I'd give up on the spot.
I found that I simply cannot believe a statement like "I can get much smarter" or "I can get much better at X", but I found that I can easily (fully, honestly, without reservations) believe that "I can get a bit more smarter", or "my intelligence is sufficient for mastering this material, so I need to push harder", or "I can get at least a bit better socially." These beliefs motivate me and make it easy for me to do the work even when it looks like progress is nonexistent. This is the meaning of believing that you can change.
Some people are extremely competent but are risk averse, so they don't take the risk that's necessary for success. Others are risk tolerant but are not very capable, so they are unlikely to succeed despite taking the risk. The trick is to be in both groups simultaneously. So if you know in your heart of hearts that you are competent, then it is worth taking the risk.
Robots are currently unable to be autonomous in any meaningful way. They still have a limited ability to perceive because computer vision is not good yet. They lack the ability to act sensibly in novel environments. Building such robots is the holy grail of AI, and it'll take a while before they are built.
A degree in mathematics requires a very large amount of effort and discipline, especially given your other obligations. Is this effort best spent on a mathematics degree, or maybe you could spend this effort differently and get what you want faster?
While point a) is a good reason to get a mathematics degree, points b) and c) are not. For point b), machine learning and statistics are much more appropriate than mathematics, and for point c), it is worth knowing that machine learning requires a fairly small subset of the mathematics you'd learn in a math degree. For example, a math degree covers many areas of mathematics (such as a heavy focus on proofs, abstract algebra, complex analysis and topology) that have no bearing on statistics and on practical machine learning. Conversely, a math degree also does not focus on statistics and probability, which are essential for data analysis.
Thus were I in your shoes, I would only study the math that is necessary to understand statistics and machine learning, and would start taking a machine learning course. The only math you need is multivariate calculus, linear algebra, and probability.
It is completely irrelevant how the world would be if everyone did X, since it will never happen. The only benefit of not using patents is a righteous feeling.
It would be much better if everyone who choose to not use patents would instead use patents to earn more money, and use this extra money to lobby their governments to change the laws. It will have a drastically higher ROI.
A person that is against patents will make no societal difference whatsoever by abstaining from them, while hurting their bottom line, so it is not worthwhile. The best way of changing patent law is by lobbying the government. Individual personal efforts of avoiding patents will be completely inconsequential.
The author is already extremely successful, and not only in his career:
" and at 40 hadn’t accomplished much other than finding a good woman who was foolish enough to marry me, and somehow managed to have two wonderful children that I was vastly unqualified to have fathered"
So all he needs to enjoy the spoils of his success is to change his point of view. It can be done. But if it is not done, then it may be impossible to truly enjoy life.
The problems with statistics is that it's complicated, both bayesian and frequentist. Specifically, all statistical methods make assumptions about the data, some of which are quite subtle and take effort to understand. Their intricacy is the reason why so many scientists use them incorrectly. It's much less about whether a method is bayesian or frequentist, but whether the specific assumptions made by a method are suitable for the data. This requires a judgement call. One of the advantages of Bayesian methods over frequentist methods is that it's easier to incorporate what we know about the data into a bayesian model using the bayesian prior straightforwardly, but only in principle, because in practice doing a good job is pretty tricky.
A minor note about point 1. It seems very easy to figure out who the real friends are through activity -- e.g., the people whose posts you comment on or whose pictures and profiles you view. So this information doesn't lose much of its value.
I believe contentment goes the other way around. The easiest way to find contentment is to help others. It has been shown that volunteering is a very reliable long-term happiness boost, for example.
It seems to me that most people want contentment and happiness in the same way most people want wealth, which is passively. I think that many of those who decide to be happier can take all proactive action to come closer to the state of mind they desire.
Not having a degree is like not dressing well for an interview. It's superficial, but people are judged for it. Largely because there are more, percentage-wise, unqualified people without degrees than with degrees.
A person may decide that it's not worth investing 4 years and lots of money for the sake of a degree, but a degree has advantages that should not be ignored.
A big reason manufacturers don't produce durable devices/clothes is because durability is invisible at purchase time. It is also hard for a manufacturer to signal durability to customers who care about it.
It is similar to the reason for which all restaurants being unhealthy --- because the healthiness of a meal is invisible. A meal may have lots of vegetables and nice-looking meat, but also lots of salt and transparent sauces that are unhealthy but tasty.
Intelligence has many aspects, of which the ability to understand complicated "analytic stuff" from books is only a part. There's also intelligence in getting things done and avoiding dumb mistakes in complicated and uncertain situations, which seem to play the greater role in startups.